Customer Journey Analytics Integration Essentials

All right. Okay. So to kick off today’s session, again, hi and welcome, everyone. My name is Katie Yoder and I work in Adobe’s customer success group as a senior strategist where we focus on helping Adobe customers get as much value as possible from their Adobe solutions. Today’s session is being recorded and a link to the recording will be sent out to everyone who registered and we will also send out the presentation slides as well to those who attended. This live webinar is largely listen only format, but is intended to be interactive and that as we go through the content in today’s session, feel free to share any questions that you have into the chat, and we reserved time at the end to address those If there are any that we don’t get to, the team will take note and follow up with you directly after today’s webinar. In addition, we’ll be sharing out a survey at the end that we’d love your participation in. Those surveys really do help us shape future sessions that we offer. All right. So again, today’s topic is customer journey analytics integration essentials. Our presenter today is Steve Palaham, senior customer success architect at Adobe. He is joined by another Adobe subject matter expert, Andy Powers, who will be assisting in the Q&A and chat module to help with answering questions as we go through today’s content. So now to get started, I will go ahead and turn things over to Steve. Great. Thanks, Katie. Can you see me or see my screen and hear me? Yep. Looks good. All right. Thanks a lot. So yeah, as Katie mentioned, my name is Steve Palaham. I’m a senior architect here on the customer success team. I’ve been with Adobe for a little bit over 13 years, so I’ve seen quite evolution. A lot of things changing from back in the day of static page code. As Katie mentioned today, we’re going to focus on customer journey analytics and really the different integrations that are available between CGA and some of the other Adobe products. I also wanted to spend a little bit of time getting my PowerPoint working. Sorry. So with that in mind, really, I wanted to talk about CGA in conjunction with some of the other Adobe products. We’re going to be talking primarily about AEP or platform solutions, but I also wanted to spend a little bit of time discussing Adobe Analytics, commerce as well, and talk over some of the, I’m going to call them integrations between CGA and non-Adobe solutions. For each of the integrations, I want to try to give just a quick overview of the product that CGA is integrating with, as well as a high-level value proposition about the integration in general. A little bit of some technical details on how the integration actually works or what it really entails, and then some practical use cases for what you can do once you actually enable these integrations. On that said, before we dive into the integration piece, I really want to talk just briefly about platform and CGA in general as products. I think it’s really important that we all have a good base and understanding about those products before we talk about the integrations and what you can unlock really by leveraging those integrations. One thing that I think is worth noting is that I am not intending to cover the nitty-gritty technical details of activating these integrations. I’m not going to provide step-by-step instructions or go through a demo of how to actually set those up. I think a lot of that has been done before, and I will intend to include in the appendix some links to that sort of documentation that you can certainly take a look at, watch some of those videos that exist, or read some of those guides that already exist for actually enabling the integrations. Instead, what I wanted to focus more on was what are they, what do you get by doing them, how do you actually get some value from those integrations. So, as I mentioned, let’s talk about AEP and CGA for just a minute. I’ll admit I’m not a huge fan of these architectural slides. I tend to glaze over a little bit when I see these pulled up, and you have probably seen this exact slide before. But hopefully I can speak to this in a way that’s going to add just a little bit of understanding and get us all started with the same level of understanding about platform. So, I think that this does do a good job of visualizing how platform works in that experience. So, right on the left-hand side, we have data collected via platform tags, and that can be data coming in from either Web SDK, Mobile SDK, Server-to-Server, or even bulk data uploads. That data comes into the edge, and then from there it gets consumed, built out into Adobe Experience Platform. Once it’s in AEP or platform, it forms datasets, and from there that data can be used and consumed by the other AEP solutions like CGA. So, at its core, we can think of platform as a souped-up data light, right? It has the capabilities to consume and ingest all of your data from all different solutions, not just Adobe solutions, not just the Adobe Web SDK, but server-to-server integrations as well as bulk data uploads. It has governance built on top of it. It has privacy controls. It has schema organization, and it all is built on XDM or the Experience data map to enable the combination of those different datasets and the consumption of that data with the rest of the Adobe solutions or even non-Adobe solutions, as you can push this data out either with event forwarding or some other tools we’ll actually talk about at the end of this. So, again, platform is just this place to collect and store all of your data built to natively work seamlessly with the rest of the Adobe solutions. So, CGA. What is CGA? How does that work? So, on the left here, we’ve got Experience Platform. That is your data that we were just talking about. Once it’s in platform, then we can start using it in CGA. Once it’s there, you can start creating these connections, which allow you to combine and stitch datasets together, and those can be used in Analysis Workspace. So, you can create a connection between your email marketing events and your digital analytics. And once you’ve done that, we’ve already created those XDM schemas so they can combine together leveraging that person ID. You can add in other datasets like profile level information from your CRM or leader opportunity stage status. You can add in lookup data, which is really just metadata. So, like marketing channels, campaign codes. And once you have all of those connections, you can create your own unique data views. And those data views are based on those connections or those datasets that have been combined or stitched together. And they enable you to define all of the dimensions and metrics that are going to be available in Analysis Workspace. So, those data views can let you change your schema settings without re-implementing anything in CGA. And this is big. We’ll talk about this a little bit later as one of those use cases. But you can change a component from metric to dimension. You can create multiple metrics with different attribution methods, different look back windows. You can create friendly metric or dimension names. And you can automatically include or exclude certain values with a specific field. Just to name like a few of the little things that you can do. These data views are really, they’re incredibly flexible with how you define the data settings for your analysis. And lastly, you know, CGA is Analysis Workspace that you’re likely very familiar with from your time using Adobe Analytics. But it’s applied to that omni-channel data. All of your data that you’ve added and included in platform, CGA can consume, build together to make flow reporting, or do all of your insight and analysis across those multiple channels. You can break down those journeys that start online and end offline. And you can apply segments from customers that are engaging in multiple channels. Oops, wrong way. So what are some of the value propositions for CGA? You know, quick time to value. CGA really allows discovering those insights and actions in seconds, instead of taking much longer to set up big SQL queries or needing that deep technical expertise, right? It’s designed to let users explore data fluidly at runtime and answer those questions open-ended, really extending the value of that underlying data and just really giving us that insight that was so hard to generate before CGA. It’s really easy for non-analysts, right? It’s not super, super easy. Not necessarily just a click of a button, but going in there, especially with the controls that exist in data views, once you’ve done the upfront work and the governance as an admin, the work as an analyst becomes much easier to just consume the data, make the reports you’re looking for, and get those insights you’re really trying to look for. And then lastly, just some precise segmentation, right? We have purpose-built AI machine learning to enhance that segment discovery and help you really drive additional activity propensity. I don’t want to dive too much into the CGA use cases. I think most of the people on this call are probably pretty familiar with CGA use cases in general. And what we want to focus on today is the integrations. But I do think just talking about these really briefly helps establish that baseline and just get us all on the same page. So call center deflection, because we have the ability to stitch online and offline data that we didn’t have before, you can really analyze what’s happening on the site that is driving your visitors to end up calling into the support center and try to fix and update those. You can do cross-device experience optimization, right? Understanding how your customers are navigating across devices to understand gaps. Marketing optimization, right? This one is not new. But just understanding the impact of your various marketing tactics, learning which ones are the best at driving conversion, and the list goes on and on and on. So that’s CGA and AEP. And hopefully that gives us just a little bit of a foundation when we’re talking about these integrations. The first one I really wanted to talk about was the integration with Adobe Analytics. So I know I promised a little bit of slide about what all of these products are, but hopefully Adobe Analytics is sort of ubiquitous enough that we don’t really need that. Analytics, while still very functional in today’s landscape, can kind of be thought of as a precursor to CGA, right? It has many of the same features, you have analysis workspace, but generally Adobe Analytics is limited to your digital properties. Yes, there are some connectors for offline data, but they’re largely add-ons and a little bit tricky to implement. I could probably not tell you the number of clients I’ve talked to about transaction IDs over the years, but I could absolutely tell you the number of clients who actually implemented a transaction ID in Adobe Analytics. But for the online channel, right, Adobe Analytics still has loads of value, and many of our clients really don’t have the time or resources to re-implement with Web SDK. But with this integration, there’s actually very, very minimal effort in setting up CGA to Adobe Analytics, sorry, Adobe Analytics into CGA via the data connector. And there’s a lot of value that you can unlock by bringing your online Adobe Analytics data that exists today into platform and being able to combine it with some of those other datasets. So really, what does the integration in Tower unlock? If you have an existing analytics implementation, the CGA to Adobe Analytics connector allows you to bring that data into AEP and into CGA as a dataset, and it unlocks all of the other features and functionality that AEP and CGA bring to the table. You’ll be able to quickly and easily combine your online and offline data and start to do true cross-channel analysis. With that in place, you’ll not only unlock new insights, but you’ll be able to find new audiences as well. One small note as well is currently, as of right now today, analytics for Target still only exists within A4T. So this integration is actually, as far as I’m aware, and Andy, you know, step in if I’m wrong here, but it’s the only way to bring that A4T data into CGA. So how does this integration actually work? It’s a lot like any other data connection for AEP, right? Except it’s plug and play. So there is some time to process historical data when you initially set this up, but it is a few clicks of a button, and your Adobe Analytics data will start populating in the platform as its own dataset. The data essentially is the hit level data that you get from analytics after it’s been augmented by FISTA and processing rules, but before it’s been updated by persistence, and then it’s batched and sent into AEP where you can access it in your views. For anyone on the call who’s super into the weeds technically, like I tend to get sometimes, it is the mid values from a debug log or from the just the raw data files from a data feed file. So then when you’re looking at the data in CGA, the data is processed at runtime to fit your view and the rules that you’ve defined for it. And again, this is just a huge difference between CGA and Adobe Analytics in general, but I think it’s really worth calling out with this integration because it enables a lot of things that you couldn’t do before with just Adobe Analytics, and it shows at least one use case or one point of value that you can get from this integration, even if you don’t have any other platform products, even if you’re not pulling in any other offline data into platform, there is still value in getting your Adobe Analytics data in CGA simply because of that runtime processing of data. So it is worth noting a little bit that there’s some latency expected here with this integration that you wouldn’t get if you re-implemented with Web SDK. Some of our clients, that’s a compelling point to do that re-implementation. But as I said, I know that many of our customers have some practicalities which prevent that. So there’s still a ton of value here and the latency is really not unreasonable at all. So, great. What can we do with this integration? Well, we can hit on something that’s kind of been a holy grail during my time in digital marketing, and that’s we can perform true cross-channel analysis, right? For e-comm clients, the value here should be pretty obvious. You can easily add and refund data, canceled order data. If you have brick and mortar, you can combine your online e-comm channel with offline brick and mortar. If you have any sort of loyalty program or first-party identifiers, you can create a truly holistic client profile and understand what your customers are doing throughout their entire funnel. For B2B clients, all of a sudden you find your sales funnel information with online leads you’ve created. You don’t have to try and qualify or score leads with assumptions that you’ve made. You can quickly, easily, and accurately know which leads have closed, how much revenue they’ve generated, and that data can feed back into your online channel for optimization, targeting, et cetera. I’m not even talking about Adobe Journey Optimizer yet, right? Just taking that data and knowing which leads are actually high value based on your close rates can really help you optimize for a lead gen site. Really, it can help you optimize as well for e-comm. If you have a product that sells well but is refunded or returned at a rate of 50% or something, that’s not a good product and that can help you get that picture that you’d otherwise not be able to get. As I previously tried to tie that offline data into Adobe Analytics, it was challenging or limited in what you could really do. But now with Platform, you can do your analysis with CJA just simply by using your existing AA data. And speaking of reusing your existing implementation, this use case also brings me a ton of joy as a technical consultant. I can’t tell you how many times we’ve started working with clients and we’ve seen that they’re still using app measurement code from four plus years ago. I’ve seen clients who are running legacy H code in their analytics implementation, right? We all know that there are practicalities, there’s time constraints, resource constraints within our daily lives at work that prevent us from doing the latest and greatest implementation. And sometimes there’s, we have to focus on sort of the lowest hanging fruit in a lot of ways. And this really is simple, right? The AA connector for CJA, it’s literally the push of a button. That’s it. Yes, ingesting other data to augment or complement it still has some effort. But the piece of bringing the existing implementation you have to CJA is really that easy. And I just find it so groundbreaking, right? Like historically, we haven’t always done that when we’ve been releasing new products, new features or things like that. The burden to implement those has often fallen to our customers. But with this, it is a click of a button. I can’t stress that enough. And I find it so valuable. Now you can take your time and your resources and focus on doing that analysis with CJA. Now, I mean, if you had the time, would I still recommend moving to Web SDK and getting off of Adobe Analytics? Probably. You may have some individual specific use cases where that doesn’t make sense outside of your resource and time constraints. But Web SDK, CJA platform, that’s the future that we’re moving toward. There’s some real performance benefits with Web SDK and other perks that sort of go along with it. But this connector is at least one area where you don’t have to sacrifice a whole lot to save a significant amount of time and effort. Instead, you get to spend that time doing your analysis, finding those insights and driving that true value and ROI for your business. Finally, the last use case I wanted to talk about is something that we’ve alluded to a little bit already. And that’s just one of the big perks of CJA is processing the data on the fly. I know this sounds a little suspicious, like isn’t one of the reasons that we preprocess analytics data so that reports can run in a reasonable time? Yes, that was true due to the way that AA processed data. But with CJA, those limitations are gone. We’ve done lots of testing. Andy, I know, has really pushed CJA to its limits and has not been able to really bog it down, make it noticeably slower or laggy or anything like that than Analysis Workspace in Adobe Analytics. So with that, we get some really big benefits with these flexible settings. If you want to update an EVAR persistence or attribution, you can do that in the data view and refresh the report. If you want one dimension to have two different persistence values side by side without any implementation updates, you can do that in CJA and just refresh the report and it will reprocess the data with those different persistence or attribution models. If you want to concatenate two values and then filter based on that combination, easy. And if you need to derive an entirely new value based on existing value or values, boom, done. It’s really that simple. And as a person that leans more technical, I feel like this just really resonates with me. In many ways, I feel like these features in CJA represent almost like a similar leap to time savings and just like the ability to rectify implementation mistakes, that moving to a tag manager from like old page code represented. Back with old page code, we had these really slow release cycles with tag managers. We got the ability to use jQuery to fix things if it was missing from a data layer. And we could release those on our own cycles independent of a true dev cycle. And these flexible settings in CJA allow you to do that same sort of thing without any implementation updates. And it also with the there’s no variable constraints anymore, right? You’re not limited to a certain number of EVAs or anything. But if you decide two months down the line that you want to change the persistence of an EVAR, you don’t have to update it in implementation. And then only start collecting data from when you made that update. Now you can just update it in CJA and just rerun the report and reprocess that data on the fly. And you’ve got that new value, that new persistence, that new attribution model, just that essentially something that would have required a big change in implementation is now done on the fly. And it really unlocks that ability to do that insight and that analysis sort of at your own pace and go in the own direction that you as an analyst want to go in. You can follow up, really pinpoint, narrow in on those really important insights. So that’s the CJA and Adobe Analytics implementation. Next, I want to talk a little bit about CJA and Adobe Journey Optimizer. So as I mentioned, you know, what is Adobe Journey Optimizer for everyone who’s just more familiar with CJA or Adobe Analytics? I like to think of it as the next iteration of Adobe Campaign or sort of Campaign Plus. You know, I’ll admit that when I first started hearing about AJO, I kind of lumped it in my head with Target, thinking that this was Adobe Target Plus, but that’s not correct. This is really a tool that was designed to answer the challenge of how do we provide clear, consistent messaging for customers throughout their entire journey and their multiple touch points with us as a business? And it does that by giving you a unified customer profile that exists within platform and that can be engaged with across a number of channels natively. And then you can also pass that information out of AJO with JSON and some other tools to engage that way with any other sort of tools that you’re using to help deliver your messaging. And really all of that helps ensure that your customers are seeing a consistent message across as many punch points, right? I know as consumers, we’ve all seen when that doesn’t go right, where you get an email telling you to buy a product that you just purchased, or you just get some different offers or ads that don’t work together. And it’s just, it can be really frustrating and disjointed and just make you feel like the business isn’t really there for you as a customer. So AJO really helps to address those challenges. So the journeys in AJO, they can be single or multi-channel, they can consist of any number of different steps or nodes within AJO, any number of different branches. And AJO is really just the engine behind that orchestration, ensuring that consistent message and making sure that you’re not getting some weird, terrible amalgamation of different or conflicting offers. If you’re familiar with Target and the challenges with conflicts in Target, AJO is something that can help us avoid that, but at a multi-channel scale instead of just in that digital sphere where Target mostly lives. So again, you identify your visitors, you adjust that information, you can orchestrate those channels, you get that omni-channel engagement, and all of this with the privacy and governance that exists natively within platform. And all of that, right, hugely important in the new ecosystem that we’re heading toward as we’re losing third-party cookies, as we’re all focused and needing to take some time to make sure we’re GDPR or CA, PA, I forgot the California acronym, but as we’re making sure that we’re adhering to all the new privacy laws that are existing. So again, what is the integration between CJA and AJO actually look like? Well, first it’s giving you your AJO data in CJA. Unsurprisingly, right, that data is another data set, just like all the data in IEP. So once it’s in platform, you’re going to unlock all those typical capabilities within CJA. You can combine that data set with your digital analytics data set, or if you did the AA connector with your Adobe analytics data set, or your offline data, and then you can do the complex reporting and analysis while including that AJO data. So what journeys are the visitors in, what steps did they get to, et cetera, et cetera, all available within CJA. Additionally, the integration of semi-bidirectional in that CJA can also publish audiences that can be consumed and used in AJO. Additionally, you can use CJA to update or add data to your profile natively, and AJO natively can consume that data. And that profile is the power behind the launch segmentation within AJO. So it’s helping us define our journeys, the flows, and the messages that we want our customers to see. So there’s a few use cases here. We’ll dive into those more in the next few slides. But high level, this is going to unlock a huge value in additional analysis capabilities within CJA, and you’re going to get a lot more information about the users that are flowing through your journeys by connecting AJO and CJA. And you can use that information in some really cool, interesting ways. So first, it’s simply just optimizing your journeys, right? AJO by default offers some reporting capabilities, but it’s not the insight engine that CJA really is. CJA is a really, really good tool, like analysis workspace, for driving those insights, digging deeper and understanding the why of something. Why is this happening? Not just the what. And leveraging that data in CJA is going to really open up all sorts of analysis that would otherwise be impossible. You can see that a visitor gets one step but not the next in AJO. But why? What happened? What did they do between those two steps? And CJA is going to help you analyze that, come up with a hypothesis for why, and refine your journey accordingly. So maybe the visitor received an email but didn’t come to the site. Maybe they came to the site but didn’t order. Well, you know, what did they do on the site? Did we send them to a landing page that was incorrect? Could we have pushed them to a different page? Was our messaging off? And that’s the question that CJA can really answer that AJO on its own is going to have a little bit trouble answering. So CJA can also help identify which steps on the journey are impacting conversion the most, and potentially enable you to remove steps or add additional steps or branches to really get more personalized content and deliver the correct information. Hopefully, you know, this use case isn’t super surprising. And it’s also very similar to how you might use the A4T integration. But like all of the platform solutions, there’s additional value with platform and CJA because you’re able to include all sorts of additional data. So the next case is sort of not too dissimilar, but trying to come at it from the opposite direction. Instead of using CJA to optimize, now we’re focusing on using CJA to help identify both new opportunities and options. And even entire journeys, right? So CJA is our main source of analysis and insight as analysts. And analysis workspace enables us to really dig and find those insights that we go beyond our gut feelings and our intuition. It helps us confirm or disprove those, yes, but also find things that we can’t really do. So AJA does a great job of answering what your visitors are doing during the journey, but it struggles, again, with that why. And so we actually have a lot of great tools to help you identify the opportunities that you’re looking for. So we have a lot of tools to help you identify the opportunities that you’re looking for. So AJA does a great job of answering what your visitors are doing during the journey, but it struggles, again, with that why. And so we can really leverage CJA here to do that additional analysis to unlock additional audiences. And even more so than we could do with Adobe Analytics because we have access to all of our data online and offline. In the past, we sort of had to assume that our visitors’ online behavior was representative of their offline behavior. Now with CJA, we don’t really have to make those assumptions. Maybe we can see that visitors that engage with our business offline stick primarily to that channel. Or maybe we can see that we respond better to different messaging once they’re online. And that by changing messaging to that specific audience, we can increase our conversion. Or we can use CJA in that case to define those new audiences or change our journeys, create entirely new journeys. The flow reporting in CJA lets us see how our visitors are interacting across all of our channels or even just online. And that is sort of a proxy, right, for the journeys that we want to be using within AJO. And so looking at that data in CJA can help you identify potential journeys that you haven’t even thought of and help. Once you identify those, you can personalize, really get that extra value out of that personalization and hopefully drive to that conversion that you’re looking for. Finally, what I wanted to talk about was relating journeys to external datasets. And what I mean by this is that AJO can tell us a lot about the journey, but the data it includes is limited to visitor profiles who are going through that specific journey. So it’s likely that you can have large, large amounts of other data that you want to be looking at. And the ability to pair that with AJO is really going to help you attribute revenue to different steps of the journey or different messaging that you’ve used within that journey and hopefully give you some more insight into what customers are doing, both before and after the journey as well. That insight into before and after the journey is also going to be really critical. That can help you expand your current journey. It can help you to find additional journeys for what it looks like after a person either falls out of the initial journey or makes it through completion and converts an initial journey. How do you want to retarget them? How do you want to keep that loyalty or that retained customer going? And then if you missed initially getting them to that journey in the first place or understanding why people are falling out early in the journey, doing that analysis on what they were doing before they even made it into that journey can be really big. So the next integration I want to talk about with CJA and RT-CDP, Real-Time Customer Data Platform. So first, real-time CDP is a data management and activation hub. Like CJA is to Adobe Analytics, RT-CDP is to Adobe Audience Manager. If you’re familiar with Audience Manager, then you are essentially familiar with RT-CDP. And if you’re not, RT-CDP and Audience Manager at their simplest do three things. First, they ingest data. Second, they allow you to collate that data into useful profiles and from those profiles create segments or audiences. And finally, you can send those audiences off to different destinations, usually advertising platforms. So data in, process in segment, data out. These profiles and audiences you create can be incredibly powerful for your advertising, for targeting, and can have some very impactful and measurable ROI. And RT-CDP enables all of this with a forward thinking approach, leaning into security, data governance, focus on first party cookies and data. You know, AAM had a very strict no PII approach and that has, that’s no longer restricted within RT-CDP because of the privacy and governance and the focus on first party data. You can actually include some of that and you can use something like a first name that you can pass along securely to your destinations and personalize those audiences with some of that information. So we’ve talked a little bit about the value of CJA, so I’m not going to hit on that too much. But RT-CDP has numerous fast, flexible data connections that power real time customer profiles. It’s got some really nice workflows, dashboards, and AI machine learning capabilities that make audience creation easy and actionable and much more robust than audience manager. The productized governance, security, and privacy is baked right in and to top it all off, your audiences can be activated anywhere. There’s even a destinations SDK for building your own destinations up. But, and you may be seeing a pattern here, the products are better together, right? With CJA and RT-CDP, you can leverage a comprehensive data set that’s further enriched with deep insights from you as an analyst to create more relevant audiences for intelligent and quick actioning, really driving the most important outcomes and driving the most value for your businesses. So the power of these applications in tandem, it’s all about the cycle of discovering the audiences, activating those audiences, and then measuring how that works out in a really seamless manner. And so when we’re talking about this integration, really at its simplest, what we’re talking about is sharing audiences that you’ve found in CJA to RT-CDP. With these together, you can really understand the complete customer experience, proactively engage those audiences with personalized experiences and meaningful solutions. So maybe you uncover a gap or an issue on the journey and need to make changes to your website to submit the process. Great. You can do that. You can identify those customers that get caught in less than ideal experience and proactively reach out. Or as you identify things that are working well, you can further engage with personalized experiences for the upsell. You can also get action from a comprehensive data set, right? Both behavioral and trait. You can combine that rich historical data with rapid activation of real-time audiences. You often have to make trade-offs when it comes to storing attributes and time periods within a CDP, but here you kind of get the best of both worlds. You can optimize your CDP and maintain that rich historical data within CJA. And when you need to engage with a specific past audience, you can do so seamlessly between the systems. And finally, because they work well together, you can really minimize the latency reduced cost from insight to activation. So the first use case with these two is focusing on winning back lapsed customers. With RT-CDP by itself, you could identify and target the lapsed customers, but you wouldn’t really be able to dig into that why they lapse. Really harping on the why with CJA today, but it’s really important. CJA, you can really quickly identify those patterns and lapsed customers and create new audiences to target an RT-CDP. You can track the user events across the full journey to see the steps that lead to a lapse, and you can easily compare data from lapsed customers to retained or loyal customers. And this all goes back to thinking about CJA as a tool for answering why something is happening, right? Once we identify that these lapsed customers exist, we can dig into that, identify possible solutions to re-engage or maybe find audiences that aren’t ever going to engage, and we can stop advertising to them and find a cost savings there. Really, there’s all sorts of tools within CJA to perform that analysis. And once you do, those audiences that you’ve identified can be shared to RT-CDP and passed along to your destinations. Next up is just the idea of advancing from consideration to purchase. With RT-CDP alone, we can identify an audience in consideration, but it’s really tricky to understand why they get stuck in that consideration phase. CJA helps us really dig into that and try to come up with some new audiences, segment out those audiences into sub-audiences, audiences that are having technical trouble, audiences that don’t like the messages, audiences that maybe need a discount to move along into the purchase. Anything like that. CJA is really going to help us dive into that and feed that back into RT-CDP and really just go through that cycle I mentioned earlier, right? Defining those audiences, consuming them and measuring. Finally, it’s worth talking about just the process optimizations and democratization of insights, right? CJA is really good at this. You don’t need to be an SQL or BI wizard to get value out of CJA and to leverage it to discover true insights for your customers’ behavior. There’s all of these tools that exist to create your data view, to curate what’s available within there and standardize so that all of your analysts are able to see the same thing. Once you make those findings and derive those insights, there’s tools to share that and socialize that so it can be quickly and easily activated on. The tools to share that audience again are super, super quick and easy. And then pushing those out to the destinations in RT-CDP is super quick and easy as well. So there’s just a ton of value here with that integration. Next up, I wanted to talk about CJA and Adobe Commerce. If you’re unfamiliar with Adobe Commerce, previously Magento, it is Adobe’s commerce solution, surprisingly, right? Store, manage, publish all of the information about your products in an easy to use and scalable way. This new integration consists of a very similar sort of integration or implementation to the Adobe Analytics Data Connector in that it is a feature that’s available to you. It is a few clicks of a button and commerce will deploy the web SDK and start tracking all of your commerce actions. And it also goes back the other way in that your audiences that you’ve defined in CJA and those profiles are going to be available in commerce for targeting. So again, you can one of these little architecture slides, but what I wanted to call out here mostly is that it’s very turnkey. You fill out a few pieces of information and it’s going to deploy the web SDK and start collecting that data. Already formatted in XTM, already with all of the events that you’re looking for. You can see add to cart, view cart, sign in and out, purchase those things already included by default. And you can take this and augment it with a more traditional digital analytics implementation on web SDK. So for clients who are just starting with commerce, this is just like a huge jumpstart on what’s available. And so again, I’m harping on this almost exact same use case we had with Adobe Analytics Connector, but I just love these easy integrations. They really bring me joy. So this one, it has a slight added benefit, I think, of in that ecom implementations are traditionally one of the trickiest parts of an analytics implementation. Being able to just flip a switch and get this data available is a huge win. You know, it’s not quite as nice as getting a full analytics implementation that you have maybe from your already existing Adobe Analytics implementation, but for net new clients especially, this can be a big win. So you get that nice quick time to value. And then additionally, once you have that value flowing into CJA, you can really start to dig into it and look for new audiences. Commerce has a number of features to recommend products to your consumer and the ability to target on those based on the audiences that you’ve defined and CJA is huge. You just, I don’t think I need to harp on that. I think the implications of that hopefully are pretty obvious. A visitor who only likes shoes, show them only shoes, right? Recommend to your visitors products that they’re actually interested in. Personalization goes such a long way to increasing conversion and ROI. This is a really great way to do that. So that sort of concludes what I’m considering the very productized integrations that exist that I’m aware of today. These integrations are constantly changing, not changing, evolving, adding new integrations. I know of several different features that are currently in betas that I’m not sure if we’re supposed to talk about or not. But the point is that there are constantly, there’s work being done to increase the different integrations that exist today and increase even the functionality for the integrations that already exist. But I did think it was worth spending a little bit of time talking about integrating CJA with non Adobe solutions. I’m using sort of, again, the term integration a little bit loosely and that these aren’t specifically productized, but they’re certainly useful and worth talking about in this context. So the first sort of integration is just getting other data into platform. Data ingestion from non Adobe resources. Like any data lake, we have tools to do that using schema definitions, using that XDM format. AEP can ingest data via web SDK. It can ingest data via the mobile SDK. You can do bulk uploads, you can do server side uploads. Some of those are fully baked out integrations that are turnkey. Some of them are going to involve mapping your data into that XDM schema and making it readable for AEP. But generally speaking, none of these aren’t, none of these are crazy challenging to get started and called out. So in a sense, I think of this as an integration, right, between any other tool that you have collecting data about your clients, your products, your site. Any of that can get ingested into platform and consumed within CJA or any of the other tools that we’ve talked about.

Next is just two steps forward getting data out of platform or CJA, specifically CJA in this case. And that is the CJA reporting API. Right, you know, loose integration, but it’s a fully automated way to export that data that you’ve built your report for. You know, you get that set up in the way that you want. You can export it with API and you can do this in an automated way so it can be pulled into any other tools or solutions that you and your company are leveraging. Additionally, for a more one-off sort of thing, there is CJA full table export. And this is sort of corollary to the data warehouse reports in Adobe Analytics, intended to be a much more sort of one-off type thing. But not every time you’re trying to get data out of CJA, do you really want to have to leverage the reporting API? Or do you want to have to have the knowledge and skills to be able to use an API at all? And the full table export fully within the UI exports that data in a way that’s easy, consumable, and you can take it and do whatever else it is that you want to do with this.

And so that’s it. That’s it for today that I have focusing on those integrations. So I’m going to send out in the appendix a few more slides with some useful information, some of those integrations I talked about, some other slides in there with links. But for now, we’ve got about six minutes left. So I wanted to just turn it over for some Q&A. Awesome. Thank you so much, Steve. That was amazing. I know personally I learned quite a bit. I think all the questions that have come through in the chat and the Q&A so far have been answered. But if there’s anything else someone would like to post, please feel free since we do have a few minutes. And I would also ask that before you roll out of the call, we do have a quick form that you can fill out just to give us some feedback on this session. If you could take the time, that would be great. But yeah, if there’s any other questions you guys want to put in the chat or Q&A, let us know. All right. Does it look like anything additional is coming through? Steve, I think you might have just overwhelmed us with the knowledge on that one. But as you guys start to think through and digest everything that Steve shared today, if questions do come up, please feel free to reach out to your Adobe account team or, you know, respond to any of the communications we’ve sent out and let us know. We look forward to helping you guys get more value out of your solutions and have a great rest of your day. Thanks, Katie. And thanks, everyone, for joining.